Using graphical text analysis to facilitate communication between customers and customer service representatives

ABSTRACT

Embodiments relate to facilitating communications between customers and customer service representatives. A method for facilitating communications between customers and customer service representatives is provided. The method generates a graph of expressions of a customer. The method generates a graph of expressions of each customer service representative of a plurality of customer service representatives. The method performs a graphical text analysis on the graph for the customer to identify an interaction style of the customer. The method performs a graphical text analysis on the graph for each customer service representative to identify an interaction style of the customer service representative. The method selects a customer service representative from the plurality of the customer service representatives based on the interaction style of the customer and the interaction styles of the plurality of the customer service representative. The method starts a communication between the customer and the selected customer service representative.

DOMESTIC PRIORITY

This application is a continuation of U.S. patent application Ser. No.14/645,659, entitled “USING GRAPHICAL TEXT ANALYSIS TO FACILITATECOMMUNICATION BETWEEN CUSTOMERS AND CUSTOMER SERVICE REPRESENTATIVES,”filed Mar. 12, 2015, the disclosure of which is incorporated byreference herein in its entirety.

BACKGROUND

The present invention relates generally to facilitating communicationsbetween customers and customer service representatives, and morespecifically to facilitating communications between the customers andthe customer service representatives based on a graphical text analysisof the expressions made by the customers and the customer servicerepresentatives.

A communication between a customer and a customer service representativein a customer service environment is often marked by inefficiency andfrustration on both sides of the communication due to a mismatch betweenthe customer service representative's communication or cognitive styleand that of the customer. Such inefficiency and frustration may beresolved or avoided if more detailed information about the customer'sdesires, intentions, emotional states, etc., is provided to the customerservice representative.

SUMMARY

Embodiments include a computer program product, a method, and a systemfor facilitating communications between customers and customer servicerepresentatives. According to an embodiment of the present invention, acomputer program product is provided. The computer program productcomprises a computer readable storage medium having program instructionsembodied therewith. The program instructions readable by a processingcircuit cause the processing circuit to perform a method of facilitatingcommunications between customers and customer service representatives.The method generates a graph of expressions of a customer. The methodgenerates a graph of expressions of each customer service representativeof a plurality of customer service representatives. The method performsa graphical text analysis on the graph for the customer to identify aninteraction style of the customer. The method performs a graphical textanalysis on the graph for each customer service representative toidentify an interaction style of the customer service representative.The method selects a customer service representative from the pluralityof the customer service representatives based on the interaction styleof the customer and the interaction styles of the plurality of thecustomer service representative. The method starts a communicationbetween the customer and the selected customer service representative.

According to another embodiment of the present invention, a method forfacilitating communications between customers and customer servicerepresentatives is provided. The method generates a graph of expressionsof a customer. The method generates a graph of expressions of eachcustomer service representative of a plurality of customer servicerepresentatives. The method performs a graphical text analysis on thegraph for the customer to identify an interaction style of the customer.The method performs a graphical text analysis on the graph for eachcustomer service representative to identify an interaction style of thecustomer service representative. The method selects a customer servicerepresentative from the plurality of the customer servicerepresentatives based on the interaction style of the customer and theinteraction styles of the plurality of the customer servicerepresentative. The method starts a communication between the customerand the selected customer service representative.

According to a further embodiment of the present invention, a computersystem for facilitating communications between customers and customerservice representatives is provided. The computer system comprises amemory having computer readable instructions and a processor configuredto execute the computer readable instructions. The instructions comprisegenerating a graph of expressions of a customer. The instructionsfurther comprise generating a graph of expressions of each customerservice representative of a plurality of customer servicerepresentatives. The instructions further comprise performing agraphical text analysis on the graph for the customer to identify aninteraction style of the customer. The instructions further compriseperforming a graphical text analysis on the graph for each customerservice representative to identify an interaction style of the customerservice representative. The instructions further comprise selecting acustomer service representative from the plurality of the customerservice representatives based on the interaction style of the customerand the interaction styles of the plurality of the customer servicerepresentative. The instructions further comprise starting acommunication between the customer and the selected customer servicerepresentative.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as embodiments of the invention isparticularly pointed out and distinctly claimed in the claims at theconclusion of the specification. The forgoing and other features, andadvantages of the embodiments are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 depicts a system for facilitating communications betweencustomers and customer service representatives according to anembodiment of the invention;

FIG. 2 depicts a graph of expressions according to an embodiment of theinvention;

FIG. 3 depicts a graph of expressions according to an embodiment of theinvention;

FIG. 4 depicts a process flow for facilitating communications betweencustomers and customer service representatives according to anembodiment of the invention;

FIG. 5 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 6 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

In some embodiments of the invention, the systems and methods perform agraphical text analysis on the expressions of a customer to identify theinteraction style of the customer. The systems and methods also performa graphical text analysis on the expressions of each of a plurality ofcustomer service representatives to identify the interaction style ofeach customer service representative. The systems and methods select acustomer service representative who has the interaction style that bestmatches the interaction style of the customer. The systems and methodsallow the selected customer service representative to communicate withthe customer to serve the needs of the customer.

During the communication, the systems and methods update the graphs forthe customer and the customer service representative and continue toperform graphical text analysis on the graphs being updated in order toidentify any change in the complementariness between the interactionstyles of the customer and the customer service representative. In someembodiments, the system and methods provide a set of prompts or aservice script to the customer service representative to guide therepresentative during the communication, or may switch the customer toanother customer service representative whose interaction style bettercomplements the current interaction style of the customer.

In this disclosure, a communication between a customer and a customerservice representative may be an electronic communication (e.g., faxes,phone calls, online audio/video/text chats, text messaging, postings onsocial media, emails, etc.), of which the content is textual or may beconverted to text. A customer is a person who consumes the services orproducts provided by a business represented by customer servicerepresentatives and may be referred to as a client. A customer servicerepresentative is a person serving the needs of the customers regardingthe services and/or the products a business offers, and are commonlyreferred to as a customer service agent, a customer supportrepresentative/agent, a help agent, etc.

An interaction style or a communication style refers to the attributesof interaction or communication that a person implicitly or explicitlyreveals through spoken and physical expressions during a conversation.Different persons have different interaction styles, and a person'sinteraction style may change over a course of a conversation. A person'sinteraction style may be defined by, e.g., interests, knowledge, goals(seeking help, providing help), desires, and cognitive and emotionalstates (e.g., anger, frustration, irritation, happiness, satisfaction,etc.).

FIG. 1 illustrates a system 100 for facilitating communications betweencustomers and customer service representatives. In some embodiments, thesystem 100 includes modules, sub-modules and datastores such as a textgenerating module 102, a text obtaining module 104, a graph constructingmodule 106, a graphical text analyzing module 108, a matching andadvising module 110, a matching and advising module 110, a customerinformation analyzing module 118, a clusters repository 112, a graphsrepository 114, an interaction styles repository 116, and a customerinformation repository 120. FIG. 1 also illustrates customers 122,customer service representatives 124, a communication system 126, andcustomer information sources 128.

The customers 122 are the customers of a business, and the customerservice representatives 124 represent the business and may communicatewith the customers 122 via the communication system 126. The customeruse communication devices (not shown) including computing devices (e.g.,smartphones, tablets, laptops, etc.), fax machines, phones, etc. toaccess the communication system 126. The computing devices may run oneor more communication tools (e.g., a web browser, a chatting softwaretool, an email client, a proprietary application provided by thebusiness, etc.) that allow the customers 122 to communicate with thecustomer service representatives 124 via the communication system 126.These communication devices are connected to the communication system126 over one or more networks (not shown). The customer servicerepresentatives 124 use communication devices that are similar to orcorrespond to the communication devices that the customers 122 use.

The communication system 126 facilitates communications between thecustomers 122 and the customer service representatives 124. Forinstance, the communication system 126 may implement a web server, anemail server, a social network service, an online chat server, a faxserver, etc. In some embodiments, the communication system 126 mayexecute one or more artificial agents that may communicate with thecustomers as customer service representatives. For example, anartificial agent may initially communicate with a customer who accessedthe communication system 126 before the customer is switched to a humancustomer service representative.

In some embodiments, the communication system 126 pushes content of thecommunications to the system 100, or the communication system 126 mayallow the system 100 to retrieve the content of the communications. Insome embodiments, the communication system 126 is remotely connected tothe system 100 via one or more networks (e.g., Internet). In otherembodiments, the communication system 126 may be part of the system 100.The content includes audible and/or textual expressions that thecustomers 122 and the customer service representatives 124 make.

The text generating module 102 transcribes the audible expressionsobtained from the communications system 126 into text. The textgenerating module 102 may use one or more now known or later developedspeech-to-text techniques for converting audible expressions into text.The text obtaining module 104 obtains the textual expressions from thecommunication system 126.

The graph constructing module 106 receives the text of the expressionsmade by the customers 122 and the customer service representatives 124from the text generating module 102 and/or the text obtaining module104. The graph constructing module 106 builds graphs from the receivedtext for the customers 122 and the customer service representatives 124.

More specifically, in some embodiments, the graph constructing module106 extracts syntactic features from the received text and converts theextracted features to vectors. These syntactic vectors may have binarycomponents for the syntactic categories such as verb, noun, pronoun,adjective, lexical root, etc. For instance, a vector [0,1,0,0 . . . ]represents a noun-word in some embodiments.

The graph constructing module 106 may also generate semantic vectorsfrom the received text using one or more now known or later developedtechniques (e.g., Latent Semantic Analysis and WordNet). The semanticcontent of each expression in the text may be represented by a vector,of which the components are determined by Singular Value Decompositionof word co-occurrence frequencies over a large database of documents.

A graph generated by the graph constructing module 106 may be in theform of: G={N, E, {hacek over (W)}}, where the nodes N represent tokens(e.g., words or phrases), the edges E represent temporal precedence inthe customer′ expressions, and each node possesses a feature vector{hacek over (W)} defined in some embodiments as a direct sum of thesyntactic and semantic vectors and additional non-textual feature vector(e.g., a predetermined vector for the identity of a customer). That is,in some embodiments, the feature vector {hacek over (W)}

s defined by the equation: {hacek over (W)}={hacek over(w)}_(sym)⊕{hacek over (w)}_(sem)⊕{hacek over (w)}_(ntxt), where {hacekover (W)} is the feature vector, {hacek over (w)}_(sym) is the syntacticvector, {hacek over (w)}_(sem) is the semantic vector, and {hacek over(w)}_(ntxt) is the non-textual features. An example graph 200 that maybe generated by the graph constructing module 106 is shown in FIG. 2. Asshown, the graph 200 is a directed graph that includes an ordered set ofexpressions (e.g., words or phrases), each with a feature vector. Loopsmay form in this graph if the same expressions are made more than once.

Referring back to FIG. 1, the graph constructing module 106 of someembodiments builds one graph that includes the expressions of thecustomers and customer service representatives in communication witheach other. Alternatively or conjunctively, the graph constructingmodule 106 builds one graph for each customer and one graph for eachcustomer service representative. In some embodiments, the graphconstructing module 106 generates graphs for the customer servicerepresentatives for all expressions the representatives make throughouta period of time (e.g., the same working day, the last one hour, thelast one week, etc.). The graph constructing module 106 stores thegenerated graphs for the customers and the customer servicerepresentatives in the graphs repository 114. The graph constructingmodule 106 also updates the graphs as more text from thecustomer-customer service representative communications is received fromthe text generating module 102 and/or the text obtaining module 104.

The graphical text analyzing module 108 performs a graphical textanalysis on each graph generated by the graph constructing module 106.As a specific example of a graphical text analysis, in some embodiments,the graphical text analyzing module 108 analyzes the graph G for acustomer or a customer service representative generated by the graphconstructing module 106 based on a variety of topological features. Thevariety of features include graph-theoretical topological measures ofthe graph skeleton (i.e., a graph without features vectors: G_(sk)={N,E}) such as degree distribution, density of small-size motifs,clustering, centrality, etc. Similarly, additional values may beextracted by including the features vectors for each node of the graph.One such instance is the magnetization of the generalized Potts model(e.g., H=

Σ_(n)E_(nm){hacek over (W)}_(n)T {hacek over (W)}_(m)) such thattemporal proximity (e.g., number of edges between two nodes) and featuresimilarity are taken into account. These features, which incorporate thesyntactic, semantic and dynamical components of the expressions, arethen combined as a multi-dimensional features vector {hacek over (F)}that represents a sample. This feature vector is finally used to train astandard classifier: M=M ({hacek over (F)}_(train), C_(train)), todiscriminate the samples that belong to different conditions C, suchthat for each sample the classifier estimates its condition identitybased on the extracted features: C(sample)=M({hacek over (F)}_(sample)).

In some embodiments, the graphical text analyzing module 108 comparesthe graph with the clusters of previously generated graphs stored in theclusters repository 112. More specifically, the feature vectors ofpreviously generated graphs with known interaction styles are plotted ina multi-dimensional text feature space to form clusters in that space.The graphical text analyzing module 108 plots the feature vectors of thegraph for the customer or the customer service representative in thespace, in order to determine whether the graph belongs to a clusterbased on, e.g., distance between the plots of the graph and the plots ofthe clusters. In some embodiments, the graphical text analyzing module108 also determines for each cluster a likelihood of the graph to belongto the cluster (e.g., the level of correlation between the graph and thecluster). If the plots of the graph are determined to fall in thefeature space of a particular cluster (e.g., for having the likelihoodof the graph belong to the cluster over a threshold), the correspondinginteraction style represented by the cluster is determined as theinteraction style of the customer. The graphical text analyzing module108 stores the identified interaction style of each customer and eachcustomer service representative in the interaction styles repository116.

The graphical text analyzing module 108 of some embodiments continuouslyperforms graphical text analysis on the graph as the graph is beingupdated by the graph constructing module 106. As a result, theinteraction style for a customer or a customer service representativemay change as the graph for the customer or the customer servicerepresentative is updated. The graphical text analyzing module 108updates the interaction styles repository 116 accordingly so that theinteraction styles repository 116 has the current interaction styles ofthe customers and the customer service representatives. In someembodiments, the interaction styles repository 116 also maintains ahistory of interaction styles for the customers and the customer servicerepresentatives. As such, the functioning of the system 100, which maybe implemented in a computer system (e.g., computer system 12 describedfurther below with reference to FIG. 5), may be improved.

The graphs constructing module 106 may also generate a composite graphfor a group of customers by, e.g., merging the graphs for individualcustomers. In some embodiments, each node of a composite graph generatedfor the group of customers indicates who is the speaker of the words orphrases of the node. The order of the conversation may be captured andpreserved in the composite graphs. The graphs constructing module 106stores the composite graphs for the groups in the graphs repository 114.FIG. 3 illustrates an example graph 300 for a group of three customers.As described above, the nodes for different customers may includeinformation about the identities of the customers. Specifically, thenodes for a customer are depicted in black, the nodes for anothercustomer are depicted in white, and the nodes for yet another customerare depicted in grey.

Referring back to FIG. 1, the graphical text analyzing module 108performs the same graphical text analysis on each composite graph toidentify a collective interaction style of a group of customers. Eachgroup of customers therefore represents a super-organism, a hive mind,or a composite person that is reflective of more than one customer. Insome embodiments, a super-organism may take the form of a customer and acustomer service representative dyad for which the customer and thecustomer service representative are analyzed as one unit. Based on suchan analysis of the super-organism dyad, the graphical text analyzingmodule 108 may make additional inferences that may be used to select aset of prompts to use or may be used to switch the customer to adifferent customer service representative, as will be described furtherbelow. In some embodiments, a customer may also be an individualcustomer teamed with a helper artificial agent with natural languageprocessing capabilities so as to form a composite customer. A customermay also be an artificial agent that acts on behalf of a human customer.Thus, the graphical text analysis is performed on linguistic output ofan artificial agent who may participate in a communication with acustomer service representative or may help a human customer.

In some embodiments, the graphical text analyzing module 108 may givedifferent weights to different customers in a group. For instance, aprimary customer in communication with a customer service representativemay receive a bigger weight than other customer in the communication.The weights may depend on various factors such as a person's position ina company, a measure of network connectivity in a social network, etc.These factors may be retrieved from the customer information repository120.

The matching and advising module 110 matches a customer with a customerservice representative based on the interaction styles of the customerand the available customer service representatives. Specifically, insome embodiments, the matching and advising module 110 identifies acustomer who accessed the communication system 126 to communicate with acustomer service representative. The matching and advising module 110finds the interaction style of this customer in the interaction stylesrepository 116 and selects a customer service representative whoseinteraction style best matches with the interaction style of thecustomer. For instance, in some embodiments, the matching and advisingmodule 110 selects a customer service representative whose interactionstyle is closest to or the same as the interaction style of thecustomer. Alternatively or conjunctively, the matching and advisingmodule 110 selects a customer service representative whose interactionstyle complements the interaction style of the customer. For example,when the interaction style of the customer indicates the customer isangered or frustrated, the matching and advising module 110 selects acustomer service representative whose interaction style indicates therepresentative is calm and patient. In some embodiments, the matchingand advising module 110 uses a predefined matching table for differentinteraction styles of the customer and different states of the customerservice representatives. The matching and advising module 110 notifiesthe communication system 126 of a match so that the communication system126 allows the customer to communicate with the selected customerservice representative.

The matching and advising module 110 also monitors the interactionstyles of the customers and the customer service representatives for anychanges or transitions from one interaction style to another as thecommunication between the matched customer and customer servicerepresentative progresses. In some embodiments, the matching andadvising module 110 determines whether the complementariness of theinteraction styles of the matched customer and customer servicerepresentative changes. The complementariness may change due to a changein the interaction style of the customer and/or due to a change in theinteraction style of the customer service representative.

The matching and advising module 110 automatically generates an adviceto present to a customer service representative in communication with acustomer based on the interaction styles in order to influence thecourse of communication between the customer and the customer servicerepresentative. The matching and advising module 110 may alert thecustomer service representative about the interaction style (e.g.,detect growing levels of irritability/anger and issue an alert) of thecustomer and suggest a set of prompts or a service script for thecustomer service representative to use to adjust the course of thecommunication. The advice may also relate to guidance on how thecustomer service representative may present information in terms of suchaspects as speed of presentation, vocal characteristics, word use,emotionality, etc. In some embodiments, the advising may be done viamessages conveyed on a head-mounted display (e.g., Google Glass),screens, earpieces, reports to customer service representative, etc.

During a communication between a customer and a customer servicerepresentative, the matching and advising module 110 may replace thecustomer service representative with another available customer servicerepresentative whose interaction style better matches the interactionstyle of the customer. In some embodiments, the alternative customerservice representative may be any of a human customer servicerepresentative, a team of customer service representatives, a questionand answer (Q&A) artificial agent (e.g., IBM's DeepQA system) withuseful natural language processing abilities, etc. The matching andadvising module 110 widens the applicability of (and serve as a “frontend” to) a Q&A artificial agent. For example, the matching and advisingmodule 110 enhances such Q&A responses, so that the information oranswers provided by the Q&A artificial agent have higher value. If thealternative customer service representative is a Q&A artificial agent,information may be emotively conveyed to a customer as useful. Forexample, the responses may be transformed into data that additionallyrepresents a simulated emotional state and potentially transmitted usingan avatar in a virtual world. Data representing an avatar that expressesthe simulated emotional state may be generated and displayed.

In some embodiments, the matching and advising module 110 automaticallyperforms confidence enhancing actions based on the customer informationstored in the external information repository 120. As described above,the interaction style of a customer identified with a likelihood or aconfidence level. When the value of confidence level is below athreshold, the matching and advising module 110 does not initiate achange in customer service representative (or getting help from an extracustomer service representative) or a change in the service script orprompts. However, if the value of confidence level is greater than thethreshold, the matching and advising module 110 automatically initiatesa change in customer service representative (or obtains a helpercustomer service representative) or a change in service script orprompts.

If the confidence level is less than the threshold, the matching andadvising module 110 automatically performs a confidence increasingaction based on the customer information, such as an analysis of othercustomer in a customer's social network (e.g., people close to thecustomer), an analysis of prior fragments of text and/or speech of thecustomer, an analysis of a prior fragments of text and/or speech ofindividuals in the customer's social network, etc. that are gathered andprocessed by the customer information analyzing module 118 from thecustomer information sources 128. The matching and advising module 110assigns various weights to the prior fragment. For example, the furtherinto the past a fragment occurs, the lower the weights of such fragmentsthe matching and advising module 110 assigns.

The matching and advising module 110 automatically performs otherconfidence increasing actions if the confidence level is smaller thanthe threshold. The matching and advising module 110 may use more publicinformation obtained about a customer, for example, posts made in asocial media account (e.g., Facebook), various public communications, ananalysis of past help queries, demographic information associated withthe customer, etc. gathered and processed by the customer informationanalyzing module 118. The use of such information may be approved in anopt-in fashion so that a customer gives permission to perform suchanalyses if he or she wishes to receive better help. Another confidenceincreasing action that the matching and advising module 110 may performis to query the customer regarding whether it is estimating thecustomer's psychological state appropriately or correctly. For example,if the customer service representative is an artificial Q&A agent, itmay ask the customer if he or she is confused or angry, in order toincrease the value of confidence level.

FIG. 4 illustrates a process flow for facilitating communicationsbetween customers and customer service representatives. In someembodiments, the system 100 performs the process flow shown in FIG. 4.At block 405, the system 100 generates a graph of expressions of acustomer. In some embodiments, the system 100 uses an artificial agentwith NLP to collect an initial set of expressions from the customer.

At block 410, the system 100 generates a graph of expressions of eachcustomer service representative of a plurality of customer servicerepresentatives. The system 100 collects expressions of each customerservice representative from other communications between the customerservice agent and other customers.

At block 415, the system 100 performs a graphical text analysis on thegraph for the customer to identify an interaction style of the customer.At block 420, the system 100 performs a graphical text analysis on thegraph for each customer service representative to identify aninteraction style of the customer service representative. In someembodiments, performing a graphical text analysis comprises determininga likelihood of a graph to belong to one of a plurality of clusters ofpreviously generated graphs. Each cluster represents one or moredifferent predefined interaction styles.

At block 425, the system 100 selects a customer service representativefrom the plurality of the customer service representatives based on theinteraction style of the customer and the interaction styles of theplurality of the customer service representative. Specifically, in someembodiments, the system 100 selects a customer service representativewho has an interaction style that best matches the interaction style ofthe customer. In some embodiments, the interaction style of a customerservice representative best matches the interaction style of thecustomer when the interaction style of the customer servicerepresentative is closest to or the same as the interaction style of thecustomer. Alternatively or conjunctively, the interaction style of acustomer service representative best matches the interaction style ofthe customer when the interaction style of the customer servicerepresentative best complements the interaction style of the customer.

At block 430, the system 100 starts a communication between the customerand the selected customer service representative. At block 435, thesystem 100 updates the graph of expressions of the customer and thegraph of expressions of the selected customer service representativeduring the communication. At block 440, the system 100 performs agraphical text analysis on the updated graphs to update interactionstyles of the customer and the selected service representative.

At block 445, in response to determining that a complementariness of theinteraction styles of the customer and the selected customer servicerepresentative is changed, the system 100 provides a set of prompts tothe selected customer service representative in order to guide theselected customer service during the communication. At block 450, inresponse to determining that the complementariness is changed, thesystem 100 switches the customer to another customer servicerepresentative who has an interaction style that better matches theupdated interaction style of the customer. In some embodiments, acustomer comprises a person teamed up with an artificial agent orcomprises a group of two or more persons. Likewise, a customer servicerepresentative comprises a person teamed up with an artificial agent orcomprises a group of two or more persons.

It is understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth hereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 5, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM p Series® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM Web Sphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and customer service communication facilitating andgraphical text analyzing.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer program product, comprising: acomputer readable storage medium having program instructions embodiedtherewith, the program instructions readable by a processing circuit tocause the processing circuit to perform a method of facilitatingcommunications between customers and customer service representatives ina customer service environment, the method comprising: performing agraphical text analysis on a graph of expressions of a customer toidentify an interaction style of the customer; performing a graphicaltext analysis on a graph of expressions for each customer servicerepresentative of a plurality of customer service representatives toidentify an interaction style of each of the plurality of customerservice representatives; selecting a customer service representativefrom the plurality of the customer service representatives based on theinteraction style of the customer and the interaction style of each ofthe plurality of customer service representatives; and starting acommunication between the customer and the selected customer servicerepresentative.
 2. The computer program product of claim 1, wherein theselecting the customer service representative comprises selecting acustomer service representative who has an interaction style that bestmatches the interaction style of the customer.
 3. The computer programproduct of claim 1, wherein the method further comprises: updating thegraph of expressions of the customer and the graph of expressions of theselected customer service representative during the communication;performing a graphical text analysis on the updated graphs to updateinteraction styles of the customer and the selected servicerepresentative; and in response to determining that a complementarinessof the interaction styles of the customer and the selected customerservice representative is changed, providing a set of prompts to theselected customer service representative in order to guide the selectedcustomer service during the communication.
 4. The computer programproduct of claim 3, wherein the method further comprises: in response todetermining that the complementariness is changed, switching thecustomer to another customer service representative who has aninteraction style that better matches the updated interaction style ofthe customer.
 5. The computer program product of claim 1, wherein themethod further comprises: using an artificial agent with naturallanguage processing (NLP) to collect an initial set of expressions fromthe customer prior to selecting a customer service representative. 6.The computer program product of claim 1, wherein performing a graphicaltext analysis comprises determining a likelihood of a graph to belong toone of a plurality of clusters of previously generated graphs, eachcluster representing one or more different predefined interactionstyles.
 7. The computer program product of claim 1, wherein the methodfurther comprises: collecting expressions of each customer servicerepresentative from other communications between the customer serviceagent and other customers prior to the communication between theselected customer service representative and the customer.
 8. Thecomputer program product of claim 1, wherein a customer comprises aperson teamed up with an artificial agent or comprises a group of two ormore persons, wherein a customer service representative comprises aperson teamed up with an artificial agent or comprises a group of two ormore persons.
 9. The computer program product of claim 1, furthercomprising: generating the graph of expressions of the customer prior toperforming the graphical text analysis on the graph of expressions ofthe customer.
 10. The computer program product of claim 1, furthercomprising: generating the graph of expressions of each customer servicerepresentative of the plurality of customer service representativesprior to performing the graphical text analysis on the graph ofexpressions for each customer service representative of the plurality ofcustomer service representatives.
 11. A computer system for a method offacilitating communications between customers and customer servicerepresentatives in a customer service environment, the systemcomprising: a memory having computer readable instructions; and aprocessor configured to execute the computer readable instructions, theinstructions comprising: performing a graphical text analysis on a graphof expressions of a customer to identify an interaction style of thecustomer; performing a graphical text analysis on a graph of expressionsfor each customer service representative of a plurality of customerservice representatives to identify an interaction style of each of theplurality of customer service representatives; selecting a customerservice representative from the plurality of the customer servicerepresentatives based on the interaction style of the customer and theinteraction style of each of the plurality of customer servicerepresentatives; and starting a communication between the customer andthe selected customer service representative.
 12. The system of claim11, wherein the selecting the customer service representative comprisesselecting a customer service representative who has an interaction stylethat best matches the interaction style of the customer.
 13. The systemof claim 11, wherein the instructions further comprise: updating thegraph of expressions of the customer and the graph of expressions of theselected customer service representative during the communication;performing a graphical text analysis on the updated graphs to updateinteraction styles of the customer and the selected servicerepresentative; and in response to determining that a complementarinessof the interaction styles of the customer and the selected customerservice representative is changed, providing a set of prompts to theselected customer service representative in order to guide the selectedcustomer service during the communication.
 14. The system of claim 13,wherein the instructions further comprise: in response to determiningthat the complementariness is changed, switching the customer to anothercustomer service representative who has an interaction style that bettermatches the updated interaction style of the customer.
 15. The system ofclaim 11, wherein the instructions further comprise: using an artificialagent with natural language processing (NLP) to collect an initial setof expressions from the customer prior to selecting a customer servicerepresentative.
 16. The system of claim 11, wherein performing agraphical text analysis comprises determining a likelihood of a graph tobelong to one of a plurality of clusters of previously generated graphs,each cluster representing one or more different predefined interactionstyles.
 17. The system of claim 11, wherein the instructions furthercomprise: generating the graph of expressions of the customer prior toperforming the graphical text analysis on the graph of expressions ofthe customer.
 18. The system of claim 11, wherein the instructionsfurther comprise: generating the graph of expressions of each customerservice representative of the plurality of customer servicerepresentatives prior to performing the graphical text analysis on thegraph of expressions for each customer service representative of theplurality of customer service representatives.